---
  title: "Computational Musicology"
  author: "Melle van der Brugge"
  output:
    flexdashboard::flex_dashboard:
      orientation: columns
      vertical_layout: fill
      storyboard: true
      selfcontained: false
      theme: 
        font: "serif"
        bg: "#f4b41a"
        fg: "#143d59" 
      social: menu
      source_code: embed
      navbar:
      - { title: "Link to playlist", href: "https://open.spotify.com/playlist/37i9dQZF1DX76Wlfdnj7AP", align: right, icon: glyphicon}
      
---

```{r setup}
library(flexdashboard)
library(tidyverse)
library(plotly)
library(spotifyr)
library(ggplot2)
library(shiny)
library(compmus)
library(knitr)
library(tidymodels)
library(heatmaply)
library(ggdendro)
```


The Playlist {data-icon="fa-music"}
===================================== 

Get your Beast Mode on!

"Beast mode refers to the aggressive, animalistic persona that one might assume when in competition or combat in order to overpower an opponent."

The beast mode playlist is a playlist with 200 songs and has around 8.8 milion likes! The Beast mode playlist made by Spotify is therefore the most popular workout playlist on Spotify

But how does a workout playlist distinguish itself from other playlists? And what are the characteristics of a workout playlist? Check out the storyboard and find out!





Storyboard {.storyboard data-icon="fa-tachometer"}
=========================================

### Introduction

blah blah blah


### The Workout Feeling

```{r clustering}
beast_mode <-
  get_playlist_audio_features("", "37i9dQZF1DX76Wlfdnj7AP")

emotionalQuadrant <- ggplot(data = beast_mode, aes(x = valence, y = energy, color = "red")) +
  geom_jitter() +
  geom_vline(xintercept = 0.5) +
  geom_hline(yintercept = 0.5) +
  scale_x_continuous(expand = c(0, 0), limits = c(0, 1)) +
  scale_y_continuous(expand = c(0, 0), limits = c(0, 1)) +
  annotate('text', 0.25 / 2, 0.95, label = "Angry / Turbulent") +
  annotate('text', 1.75 / 2, 0.95, label = "Joyful / Happy") +
  annotate('text', 1.75 / 2, 0.05, label = "Peace / Chill") +
  annotate('text', 0.25 / 2, 0.05, label = "Depressing / Sad") +
  labs(x= "Valence", y= "Energy") +
  ggtitle("Emotional quadrant Beast mode", "Based on energy y valence")  
emotionalQuadrant
```

*** 

In the figure "Emotional quadrant Beast mode" you can see what feeling the Beast mode playlist gives. Do songs convey peace, joy, anger, sadness? We can answer this question by looking at two measures Spotify has established: Valence and Energy.

Defined by Spotify, valence defines it as follows: A measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track. Tracks with high valence sound more positive (e.g. happy, cheerful, euphoric), while tracks with low valence sound more negative (e.g. sad, depressed, angry). On the other hand, energy defines it as follows: Energy is a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity. Typically, energetic tracks feel fast, loud, and noisy. For example, death metal has high energy, while a Bach prelude scores low on the scale. Perceptual features contributing to this attribute include dynamic range, perceived loudness, timbre, onset rate, and general entropy.

With these two measurements, you can create a quadrant with a scatter plot that reveals this information. As you can see, the Beast mode playlist is high in Energy and Valence has a nice distribution.


### The Remix Effect

```{r trust nobody}
trust_nobody_normal <- get_tidy_audio_analysis("08bDGEzsYd9ebCc7p23X4j") %>%
      select(segments) %>%
      unnest(segments) %>%
      select(start, duration, pitches)

trust_nobody_remix_valentino_khan <- get_tidy_audio_analysis("52wqMbmCqeTw37jd4zFAWr") %>%
      select(segments) %>%
      unnest(segments) %>%
      select(start, duration, pitches)

compmus_long_distance(
  trust_nobody_normal %>% mutate(pitches = map(pitches, compmus_normalise, "chebyshev")),
  trust_nobody_remix_valentino_khan %>% mutate(pitches = map(pitches, compmus_normalise, "chebyshev")),
  feature = pitches,
  method = "aitchison"
) %>%
  ggplot(
    aes(
      x = xstart + xduration / 2,
      width = xduration,
      y = ystart + yduration / 2,
      height = yduration,
      fill = d
    )
  ) +
  geom_tile() +
  coord_equal() +
  labs(x = "Trust nobody", y = "Trust Nobody Valentino Khan Remix") +
  theme_minimal() +
  scale_fill_viridis_c(guide = NULL)
```

***






### Slow vs Fast


***

Montero and Element are the two songs with the highest and lowest tempo in the beast mode playlist. You can see the clear difference between the two

### Learn More
jvhjhv



Credits {data-icon="fa-info"}
=====================================